Todoist AI MCP Server

Todoist AI and MCP SDK

Library for connecting AI agents to Todoist. Includes tools that can be integrated into LLMs, enabling them to access and modify a Todoist account on the user's behalf.

These tools can be used both through an MCP server, or imported directly in other projects to integrate them to your own AI conversational interfaces.

Using tools

1. Add this repository as a dependency

npm install @doist/todoist-ai

2. Import the tools and plug them to an AI

Here's an example using Vercel's AI SDK.

import { findTasksByDate, addTasks } from "@doist/todoist-ai";
import { TodoistApi } from "@doist/todoist-api-typescript";
import { streamText } from "ai";

// Create Todoist API client
const client = new TodoistApi(process.env.TODOIST_API_KEY);

// Helper to wrap tools with the client
function wrapTool(tool, todoistClient) {
    return {
        ...tool,
        execute(args) {
            return tool.execute(args, todoistClient);
        },
    };
}

const result = streamText({
    model: yourModel,
    system: "You are a helpful Todoist assistant",
    tools: {
        findTasksByDate: wrapTool(findTasksByDate, client),
        addTasks: wrapTool(addTasks, client),
    },
});

Using as an MCP server

Quick Start

You can run the MCP server directly with npx:

npx @doist/todoist-ai

Setup Guide

The Todoist AI MCP server is available as a streamable HTTP service for easy integration with various AI clients:

Primary URL (Streamable HTTP): https://ai.todoist.net/mcp

Claude Desktop

1. Open Settings → Connectors → Add custom connector
2. Enter https://ai.todoist.net/mcp and complete OAuth authentication

Cursor

Create a configuration file:

• Global: ~/.cursor/mcp.json
• Project-specific: .cursor/mcp.json

{
  "mcpServers": {
    "todoist": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://ai.todoist.net/mcp"]
    }
  }
}

Then enable the server in Cursor settings if prompted.

Claude Code (CLI)

Firstly configure Claude so it has a new MCP available using this command:

claude mcp add --transport http todoist https://ai.todoist.net/mcp

Then launch claude, execute /mcp, then select the todoist MCP server.

This will take you through a wizard to authenticate using your browser with Todoist. Once complete you will be able to use todoist in claude.

Visual Studio Code

1. Open Command Palette → MCP: Add Server
2. Select HTTP transport and use:

{
  "servers": {
    "todoist": {
      "type": "http",
      "url": "https://ai.todoist.net/mcp"
    }
  }
}

Other MCP Clients

npx -y mcp-remote https://ai.todoist.net/mcp

For more details on setting up and using the MCP server, including creating custom servers, see docs/mcp-server.md.

Features

A key feature of this project is that tools can be reused, and are not written specifically for use in an MCP server. They can be hooked up as tools to other conversational AI interfaces (e.g. Vercel's AI SDK).

This project is in its early stages. Expect more and/or better tools soon.

Nevertheless, our goal is to provide a small set of tools that enable complete workflows, rather than just atomic actions, striking a balance between flexibility and efficiency for LLMs.

For our design philosophy, guidelines, and development patterns, see docs/tool-design.md.

Available Tools

For a complete list of available tools, see the src/tools directory.

OpenAI MCP Compatibility

This server includes search and fetch tools that follow the OpenAI MCP specification, enabling seamless integration with OpenAI's MCP protocol. These tools return JSON-encoded results optimized for OpenAI's requirements while maintaining compatibility with the broader MCP ecosystem.

Dependencies

• MCP server using the official @modelcontextprotocol/sdk
• Todoist Typescript API client @doist/todoist-api-typescript

MCP Server Setup

See docs/mcp-server.md for full instructions on setting up the MCP server.

Local Development Setup

See docs/dev-setup.md for full setup instructions and CONTRIBUTING.md for contributor workflows and quality checks.

Widgets

This project includes support for MCP Apps – interactive UI widgets rendered inline in AI chat interfaces. Widgets provide rich visual representations of tool outputs (e.g., task lists) instead of plain text.

See docs/widgets.md for the widget architecture, build pipeline, and development workflow.

Quick Start

After cloning and setting up the reposit